Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use bsen26/setfit-multilabel-example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use bsen26/setfit-multilabel-example with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("bsen26/setfit-multilabel-example") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use bsen26/setfit-multilabel-example with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("bsen26/setfit-multilabel-example") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Download model_head.pkl from bsen26/setfit-multilabel-example: direct link, hf CLI and curl.
- Browser
- Download file 52.8 kB
-
https://huggingface.co/bsen26/setfit-multilabel-example/resolve/main/model_head.pkl
- Command line
-
hf download hf://bsen26/setfit-multilabel-example/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/bsen26/setfit-multilabel-example/resolve/main/model_head.pkl
52.8 kB
- Xet hash:
- 7fc239cd17663c2a92f28529d7ea6f036dec4698821185cd273cd9ca8a808cc5
- Size of remote file:
- 52.8 kB
- SHA256:
- dcc12431912fdcf8e8fef935e35392b4609a7e47da3da13bccde8cc63ced8543
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